A Hybrid Multiobjective Differential Evolution Algorithm and Its Application to the Optimization of Grinding and Classification
The grinding-classification is the prerequisite process for full recovery of the nonrenewable minerals with both production quality and quantity objectives concerned. Its natural formulation is a constrained multiobjective optimization problem of complex expression since the process is composed of o...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
|
| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2013/841780 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849306849605582848 |
|---|---|
| author | Yalin Wang Xiaofang Chen Weihua Gui Chunhua Yang Lou Caccetta Honglei Xu |
| author_facet | Yalin Wang Xiaofang Chen Weihua Gui Chunhua Yang Lou Caccetta Honglei Xu |
| author_sort | Yalin Wang |
| collection | DOAJ |
| description | The grinding-classification is the prerequisite process for full recovery of the nonrenewable minerals with both production quality and quantity objectives concerned. Its natural formulation is a constrained multiobjective optimization problem of complex expression since the process is composed of one grinding machine and two classification machines. In this paper, a hybrid differential evolution (DE) algorithm with multi-population is proposed. Some infeasible solutions with better performance are allowed to be saved, and they participate randomly in the evolution. In order to exploit the meaningful infeasible solutions, a functionally partitioned multi-population mechanism is designed to find an optimal solution from all possible directions. Meanwhile, a simplex method for local search is inserted into the evolution process to enhance the searching strategy in the optimization process. Simulation results from the test of some benchmark problems indicate that the proposed algorithm tends to converge quickly and effectively to the Pareto frontier with better distribution. Finally, the proposed algorithm is applied to solve a multiobjective optimization model of a grinding and classification process. Based on the technique for order performance by similarity to ideal solution (TOPSIS), the satisfactory solution is obtained by using a decision-making method for multiple attributes. |
| format | Article |
| id | doaj-art-1ecf24400e45442b92b638e12df96b51 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-1ecf24400e45442b92b638e12df96b512025-08-20T03:54:57ZengWileyJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/841780841780A Hybrid Multiobjective Differential Evolution Algorithm and Its Application to the Optimization of Grinding and ClassificationYalin Wang0Xiaofang Chen1Weihua Gui2Chunhua Yang3Lou Caccetta4Honglei Xu5School of Information Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Information Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Information Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Information Science and Engineering, Central South University, Changsha 410083, ChinaDepartment of Mathematics & Statistics, Curtin University, Perth, WA 6845, AustraliaDepartment of Mathematics & Statistics, Curtin University, Perth, WA 6845, AustraliaThe grinding-classification is the prerequisite process for full recovery of the nonrenewable minerals with both production quality and quantity objectives concerned. Its natural formulation is a constrained multiobjective optimization problem of complex expression since the process is composed of one grinding machine and two classification machines. In this paper, a hybrid differential evolution (DE) algorithm with multi-population is proposed. Some infeasible solutions with better performance are allowed to be saved, and they participate randomly in the evolution. In order to exploit the meaningful infeasible solutions, a functionally partitioned multi-population mechanism is designed to find an optimal solution from all possible directions. Meanwhile, a simplex method for local search is inserted into the evolution process to enhance the searching strategy in the optimization process. Simulation results from the test of some benchmark problems indicate that the proposed algorithm tends to converge quickly and effectively to the Pareto frontier with better distribution. Finally, the proposed algorithm is applied to solve a multiobjective optimization model of a grinding and classification process. Based on the technique for order performance by similarity to ideal solution (TOPSIS), the satisfactory solution is obtained by using a decision-making method for multiple attributes.http://dx.doi.org/10.1155/2013/841780 |
| spellingShingle | Yalin Wang Xiaofang Chen Weihua Gui Chunhua Yang Lou Caccetta Honglei Xu A Hybrid Multiobjective Differential Evolution Algorithm and Its Application to the Optimization of Grinding and Classification Journal of Applied Mathematics |
| title | A Hybrid Multiobjective Differential Evolution Algorithm and Its Application to the Optimization of Grinding and Classification |
| title_full | A Hybrid Multiobjective Differential Evolution Algorithm and Its Application to the Optimization of Grinding and Classification |
| title_fullStr | A Hybrid Multiobjective Differential Evolution Algorithm and Its Application to the Optimization of Grinding and Classification |
| title_full_unstemmed | A Hybrid Multiobjective Differential Evolution Algorithm and Its Application to the Optimization of Grinding and Classification |
| title_short | A Hybrid Multiobjective Differential Evolution Algorithm and Its Application to the Optimization of Grinding and Classification |
| title_sort | hybrid multiobjective differential evolution algorithm and its application to the optimization of grinding and classification |
| url | http://dx.doi.org/10.1155/2013/841780 |
| work_keys_str_mv | AT yalinwang ahybridmultiobjectivedifferentialevolutionalgorithmanditsapplicationtotheoptimizationofgrindingandclassification AT xiaofangchen ahybridmultiobjectivedifferentialevolutionalgorithmanditsapplicationtotheoptimizationofgrindingandclassification AT weihuagui ahybridmultiobjectivedifferentialevolutionalgorithmanditsapplicationtotheoptimizationofgrindingandclassification AT chunhuayang ahybridmultiobjectivedifferentialevolutionalgorithmanditsapplicationtotheoptimizationofgrindingandclassification AT loucaccetta ahybridmultiobjectivedifferentialevolutionalgorithmanditsapplicationtotheoptimizationofgrindingandclassification AT hongleixu ahybridmultiobjectivedifferentialevolutionalgorithmanditsapplicationtotheoptimizationofgrindingandclassification AT yalinwang hybridmultiobjectivedifferentialevolutionalgorithmanditsapplicationtotheoptimizationofgrindingandclassification AT xiaofangchen hybridmultiobjectivedifferentialevolutionalgorithmanditsapplicationtotheoptimizationofgrindingandclassification AT weihuagui hybridmultiobjectivedifferentialevolutionalgorithmanditsapplicationtotheoptimizationofgrindingandclassification AT chunhuayang hybridmultiobjectivedifferentialevolutionalgorithmanditsapplicationtotheoptimizationofgrindingandclassification AT loucaccetta hybridmultiobjectivedifferentialevolutionalgorithmanditsapplicationtotheoptimizationofgrindingandclassification AT hongleixu hybridmultiobjectivedifferentialevolutionalgorithmanditsapplicationtotheoptimizationofgrindingandclassification |